code: def cross_squared_distance_matrix(x, y):
      """Pairwise squared distance between two (batch) matrices' rows (2nd dim).
  
      Computes the pairwise distances between rows of x and rows of y
      Args:
      x: [batch_size, n, d] float `Tensor`
      y: [batch_size, m, d] float `Tensor`
      Returns:
      squared_dists: [batch_size, n, m] float `Tensor`, where
      squared_dists[b,i,j] = ||x[b,i,:] - y[b,j,:]||^2
      """
      x_norm_squared = torch.sum(torch.mul(x, x))
      y_norm_squared = torch.sum(torch.mul(y, y))
  
      x_y_transpose = torch.matmul(
          x.squeeze(0), y.squeeze(0).transpose(0, 1)
      )
  
      # squared_dists[b,i,j] = ||x_bi - y_bj||^2 = x_bi'x_bi- 2x_bi'x_bj + x_bj'x_bj
      squared_dists = (
          x_norm_squared - 2 * x_y_transpose + y_norm_squared
      )
  
      return squared_dists.float()
  
file_path: espnet/espnet/utils/spec_augment.py
imports: import random
  import torch
label_desc: Use image node_id 12 for calling a global function with example usage: cross_squared_distance_matrix(x, y) and returns: squared_dists